Biology subjects

Addagudi, S.

Publications and source records attributed to Addagudi, S..

3 recordsLinked to original sources

bioRxiv2025

Rapid and Reproducible Multimodal Biological Foundation Model Development with AIDO.ModelGenerator

Foundation models (FMs) for DNA, RNA, proteins, cells, and tissues have begun to close long-standing performance gaps in biological prediction tasks, yet each modality is usually studied in isolation. Bridging them requires software that can ingest heterogeneous data, apply large pre-trained backbones from various sources, and perform multimodal benchmarking studies at scale. We present AIDO.ModelGenerator, an open-source toolkit that turns these needs into declarative experiment recipes through a structured experimental framework. AIDO.ModelGenerator provides (i) 300+ datasets covering DNA, RNA, protein, cell, spatial, and multimodal data types; (ii) 30+ pretrained FMs ranging from 3M to 16B parameters; (iii) 10+ plug-and-play use-cases covering inference, adaptation, prediction, generation, and zero-shot evaluation; and (iv) YAML-driven experiment recipes that enable exact reproducibility. On a sequence-to-expression prediction task, AIDO.ModelGenerator systematically builds and tests unimodal and multimodal models, achieving a new SOTA by combining DNA and RNA FMs that outperforms unimodal baselines by over 10%. In a Crohns disease case-study, the frameworks simulated knockout protocol ranks the clinically implicated target SOX4 6,000 positions higher than differential-expression baselines, illustrating its utility for therapeutic target discovery. We release code, tutorials, checkpoints, datasets, and API reference to accelerate multimodal FM research in the life sciences1.

bioinformatics↗

bioRxiv2025

Multimodal Benchmarking of Foundation Model Representations for Cellular Perturbation Response Prediction

The decreasing cost of single-cell RNA sequencing (scRNA-seq) has enabled the collection of massive scRNA-seq datasets, which are now being used to train transformer-based cell foundation models (FMs). One of the most promising applications of these FMs is perturbation response modeling. This task aims to forecast how cells will respond to drugs or genetic interventions. Accurate perturbation response models could drastically accelerate drug discovery by reducing the space of interventions that need to be tested in the wet lab. However, recent studies have shown that FM-based models often struggle to outperform simpler baselines for perturbation response prediction. A key obstacle is the lack of understanding of the components driving performance in FM-based perturbation response models. In this work, we conduct the first systematic pan-modal study of perturbation embeddings, with an emphasis on those derived from biological FMs. We benchmark their predictive accuracy, analyze patterns in their predictions, and identify the most successful representation learning strategies. Our findings offer insights into what FMs are learning and provide practical guidance for improving perturbation response modeling.

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bioRxiv2024

Scaling Dense Representations for Single Cell with Transcriptome-Scale Context

Developing a unified model of cellular systems is a canonical challenge in biology. Recently, a wealth of public single-cell RNA sequencing data as well as rapid scaling of self-supervised learning methods have provided new avenues to address this longstanding challenge. However, rapid parameter scaling has been essential to the success of large language models in text and images, while similar scaling has not been attempted with Transformer architectures for cellular modeling. To produce accurate, transferable, and biologically meaningful representations of cellular systems, we develop AIDO.Cell, a pretrained module for representing gene expression and cellular systems in an AI-driven Digital Organism [1]. AIDO.Cell contains a series of 3M, 10M, 100M, and 650M parameter encoder-only dense Transformer models pre-trained on 50 million human cells from diverse tissues using a read-depth-aware masked gene expression pretraining objective. Unlike previous models, AIDO.Cell is capable of handling the entire human transcriptome as input without truncation or sampling tricks, thus learning accurate and general representations of the human cells entire transcriptional context. This pretraining with a longer context was enabled through FlashAttention-2, mixed precision, and large-scale distributed systems training. AIDO.Cell (100M) achieves state-of-the-art results in tasks such as zero-shot clustering, cell-type classification, and perturbation modeling. Our findings reveal interesting loss scaling behaviors as we increase AIDO.Cells parameters from 3M to 650M, providing insights for future directions in single-cell modeling. Models and code are available through ModelGenerator in https://github.com/genbio-ai/AIDO and on Hugging Face.

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